Effectiveness of Design and Implementation Alternatives for Stormwater Control Measures Modeled at the Watershed Scale.

To evaluate the effectiveness of dispersed stormwater control measures (SCMs), it is important to consider groundwater-surface water interactions and their consequences for stream hydrologic responses relevant to channel geomorphic stability and ecology. This study aimed to evaluate the effectiveness of different SCM design scenarios and implementation alternatives on exceedance levels and volumes of streamflow at the watershed scale. For this purpose, a process-based block-connector model of Sligo Creek, an urban watershed (29 km2) in the suburbs of Washington, DC, was used to study the effects of SCM system design on the stream hydrograph. The watershed has 34% impervious area (IA), which was discretized into 14 similar-sized subwatersheds, each consisting of pervious and impervious surface areas. Each subwatershed was compartmentalized with the representative overland flow, unsaturated flow, groundwater blocks, and links to main channel segments. The model was calibrated and validated to existing conditions using a 3-year time series of observed flow data. Afterward, a predevelopment simulation was configured. Three SCM unit designs and IA diversions through the SCM retrofit system were simulated. The three unit design scenarios represented a simple pond with surface storage and overflow or SCMs that infiltrate with an engineered soil layer and with or without an underdrain pipe. Differences among the model simulations were evaluated using flow exceedance probability curves. The area of the SCM system was modeled as 5% of the IA retrofit. Three implementation levels, including 10%, 50%, and 90% of the IA diverted through SCMs, were considered for each SCM unit design. The results showed that at least a 50% retrofit of runoff from IA watershedwide would be needed to achieve similar predevelopment base flows and peak flows. Intermediate flows could not be matched but were closest for the infiltration with the underdrain pipe design scenario. It was also found that concentrating the SCMs in the lower portion of the watershed resulted in more effectively achieving the predeveloped exceedance curves than uniform SCM implementation. The results are relevant to planning-level decisions that depend on effectiveness predictions of different SCM unit designs and implementation alternatives in developed watersheds.

Introducing bioflocculation boundaries in process control to enhance effluent quality of high-rate contact-stabilization systems.

High-rate activated sludge (HRAS) systems suffer from high variability of effluent quality, clarifier performance, and carbon capture. This study proposed a novel control approach using bioflocculation boundaries for wasting control strategy to enhance effluent quality and stability while still meeting carbon capture goals. The bioflocculation boundaries were developed based on the oxygen uptake rate (OUR) ratio between contactor and stabilizer (feast/famine) in a high-rate contact stabilization (CS) system and this OUR ratio was used to manipulate the wasting setpoint. Increased oxidation of carbon or decreased wasting was applied when OUR ratio was <0.52 or >0.95 to overcome bioflocculation limitation and maintain effluent quality. When no bioflocculation limitations (OUR ratio within 0.52-0.95) were detected, carbon capture was maximized. The proposed control concept was shown for a fully automated OUR-based control system as well as for a simplified version based on direct waste flow control. For both cases, significant improvements in effluent suspended solids level and stability (<50-mg TSS/L), solids capture over the clarifier (>90%), and COD capture (median of 32%) were achieved. This study shows how one can overcome the process instability of current HRAS systems and provide a path to achieve more reliable outcomes. PRACTITIONER POINTS: Online bioflocculation boundaries (upper and lower limit) were defined by the OUR ratio between contactor and stabilizer (feast/famine). To maintain effluent quality, carbon oxidation was minimized when bioflocculation was not limited (0.52-0.95 OUR ratio) and increased otherwise. A fully automated control concept was piloted, also a more simplified semiautomated option was proposed. Wasting control strategies with bioflocculation boundaries improved effluent quality while meeting carbon capture goals. Bioflocculation boundaries are easily applied to current wasting control schemes applied to HRAS systems (i.e., MLSS, SRT, and OUR controls).

Towards more predictive clarification models via experimental determination of flocculent settling coefficient value.

Improved settleability has become an essential feature of new wastewater treatment innovations. To accelerate adoption of such new technologies, improved clarifier models are needed to help with designing and predicting improvement in settleability. In general, the level of mathematics of settling clarifier models has gone far beyond the level of existing experimental methods available to support these models. To date, even for simple one-dimensional (1D) clarifier models, no experimental method has been described for flocculent settling coefficient (rp). As a consequence, rp cannot be considered as a sludge characteristic and is used as a calibration parameter to achieve observed effluent quality. In this study, we focused on the development of an empirical function based on a simple and practical experimental approach for the calculation of the rp value from sludge characteristics. This approach provided a similar approach as currently taken for hindered settling coefficient calculations (Veslind equation) and allowed for the model to predict effluent quality, thus increasing the power of the 1D model. The threshold of flocculation (TOF), which describes the collision efficiency of particles, directly correlated with the effluent quality of the five tested activated sludge systems and was selected as experimental method. The proposed empirical function between TOF and rp was validated for four years of validating data with five different sludge types operated under different operational conditions and configurations. The good effluent quality prediction with this approach brings us one step closer in making the clarification models more predictive towards effluent quality and clarifier performance.

Complexity of groundwater age mixing near a seawater intrusion zone based on multiple tracers and Bayesian inference.

Aquifer flow systems near seawater interfaces can be complicated by density-driven flows and the formation of stagnation zones, which inevitably introduces uncertainty into groundwater age-dating. While age-dating has proved effective to understand the seawater intrusion and aquifer salinization process in coastal aquifers, further efforts are needed to propagate model and data uncertainty to the uncertainty associated with the inferred age distributions. This study was performed in a coastal aquifer located close to the Yellow Sea, South Korea, where there is a decreasing trend of groundwater levels due to recent heavy exploitation, raising a warning of induced seawater intrusion. We inferred the groundwater age distributions in wells around the intrusion zone and estimated the uncertainty associated with the inference based on multiple age tracers including 3H, tritiogenic 3He, radiogenic 4He, CFC-11, CFC-12 and CFC-113 using Bayesian inference. We examined various models representing the age distributions including traditional parametric Lumped Parameter Models (LPMs) and two non-parametric "shape-free" models. The results showed that the mean ages at the study site ranged from 10.9 to 522.5 y. Complex, multimodal distributions of ages occurred near a seawater intrusion area and upland recharge zones, implying converging paths of a wide range of different ages in those regions. In particular, the age distributions estimated near the seawater intrusion interface were characterized by heavy-tailed mixing structures with elevated concentrations of 4He. This likely indicates density-driven upward flow at the seawater intrusion interface, forcing old groundwater rich in 4He into the shallow aquifer. The Bayesian inference estimated large uncertainties particularly for the old age distributions, which was attributed partly to the gradual accumulation of 4He in groundwater. The Bayesian inference improved understanding of flow dynamics at a complex seawater interface and identified opportunities to further reduce uncertainty of old water age estimates that characterize upwelling groundwater near the interface.

Nitrate residual as a key parameter to efficiently control partial denitrification coupling with anammox.

Despite the increased research efforts, full-scale implementation of shortcut nitrogen removal strategies has been challenged by the lack of consistent nitrite-oxidizing bacteria out-selection. This paper proposes an alternative path using partial denitrification (PdN) selection coupled with anaerobic ammonium-oxidizing bacteria (AnAOB). A nitrate residual concentration (>2 mg N/L) was identified as the crucial factor for metabolic PdN selection using acetate as a carbon source, unlike the COD/N ratio which was often suggested. Therefore, a novel and simple acetate dosing control strategy based on maintaining a nitrate concentration was tested in the absence and presence of AnAOB, achieving PdN efficiencies above 80%. The metabolic-based PdN selection allowed for flexibility to move between PdN and full denitrification when required to meet effluent nitrate levels. Due to the independence of this strategy on species selection and management of nitrite competition, this novel approach will guarantee nitrite availability for AnAOB under mainstream conditions unlike shortcut nitrogen removal approaches based on NOB out-selection. Overall, a COD addition of only 2.2 g COD/g TIN removed was needed for the PdN-AnAOB concept showing its potential for significant savings in external carbon source needs to meet low TIN effluent concentrations making this concept a competitive alternative. PRACTITIONER POINTS: Nitrate residual is the key control parameter for partial denitrification selection. Metabolic selection allowed for flexibility of moving from partial to full denitrification. 2.2 g COD/g TIN removed was needed for partial denitrification-anammox process.

Impact of carbon source and COD/N on the concurrent operation of partial denitrification and anammox.

In this study, concurrent operation of anammox and partial denitrification within a nonacclimated mixed culture system was proposed. The impact of carbon sources (acetate, glycerol, methanol, and ethanol) and COD/NO3- -N ratio on partial denitrification selection under both short- and long-term operations was investigated. Results from short-term testing showed that all carbon sources supported partial denitrification. However, acetate and glycerol were preferred due to their display of efficient partial denitrification selection, which may be related to their different electron transport pathways in comparison with methanol. Long-term operation confirmed results of batch tests by showing the contribution of partial denitrification to nitrate removal above 90% after acclimation in both acetate and glycerol reactors. In contrast, methanol showed challenges of maintaining efficient partial denitrification. COD/NO3- -N ratio mainly controlled the rate of nitrate reduction and not directly partial denitrification selection; thus, it should be used to balance between denitrification rate and anammox rate. PRACTITIONER POINTS: The authors aimed to investigate the impact of carbon sources and COD/NO3-N ratio on partial denitrification selection. All the carbon sources supported partial denitrification as long as the nitrite sink was available. 90% partial denitrification could be achieved with both acetate and glycerol in long-term operations. COD/NO3-N ratio did not directly control partial denitrification but can be used to balance between denitrification rate and anammox rate.

Stable Isotopes and Bayesian Modeling Methods of Tracking Sources and Differentiating Bioavailable and Recalcitrant Phosphorus Pools in Suspended Particulate Matter.

Understanding the sources of different phosphorus (P) pools and their bioavailability under imposed biogeochemical environments in a watershed is limited largely due to the lack of appropriate methods. In this research, phosphate oxygen isotope ratios and Bayesian modeling on fingerprinting elements were applied as two novel methods to identify sources and relative recalcitrancy of particulate P pools suspended in water in the continuum of sources from land to the mouth of a coastal estuary to the Chesapeake Bay. Comparative analyses of sizes, relative ratios, and oxygen isotope values of particulate P pools in the creek water suggested that the NaHCO3-P pool was bioavailable, whereas NaOH-P and HCl-P pools were recalcitrant during P transport along the creek. Agricultural field soil, streambank, and river bottom sediments were major sources of particulate P and their contributions varied significantly at the headwater and downstream regions of the creek. Bayesian modeling based on fingerprinting elements suggested that tides played a major role in forming particulate matter from estuarine sources at the creek mouth region and importing it upstream. These findings provide new insights into the origin and fate of particulate P and the fidelity of isotope and fingerprinting methods in source tracking of P in tidally influenced watersheds.

Evaluation of bacterial run and tumble motility parameters through trajectory analysis.

In this paper, a method for extraction of the behavior parameters of bacterial migration based on the run and tumble conceptual model is described. The methodology is applied to the microscopic images representing the motile movement of flagellated Azotobacter vinelandii. The bacterial cells are considered to change direction during both runs and tumbles as is evident from the movement trajectories. An unsupervised cluster analysis was performed to fractionate each bacterial trajectory into run and tumble segments, and then the distribution of parameters for each mode were extracted by fitting mathematical distributions best representing the data. A Gaussian copula was used to model the autocorrelation in swimming velocity. For both run and tumble modes, Gamma distribution was found to fit the marginal velocity best, and Logistic distribution was found to represent better the deviation angle than other distributions considered. For the transition rate distribution, log-logistic distribution and log-normal distribution, respectively, was found to do a better job than the traditionally agreed exponential distribution. A model was then developed to mimic the motility behavior of bacteria at the presence of flow. The model was applied to evaluate its ability to describe observed patterns of bacterial deposition on surfaces in a micro-model experiment with an approach velocity of 200 µm/s. It was found that the model can qualitatively reproduce the attachment results of the micro-model setting.

Modeling of biotic and abiotic processes affecting phosphate oxygen isotope ratios in a mineral-water-biota system.

Abiotic and biotic reactions operate side by side in the cycling of phosphorus (P) in the environment, but the relative roles of these two reactions vary both spatially and temporally. In biotic reactions, the uptake and release of P are catalyzed by enzymes and thus change phosphate oxygen isotope ratios, while in abiotic reactions, the absence of hydrolysis-condensation reactions results in no apparent changes in isotope composition, except short-term kinetic isotope effect due solely to preferential ion exchange. Therefore, isotope method could be a promising tool to differentiate relative roles of these two reactions in the environment but the relationship of the dynamic concentration and isotope exchange at the biota-water interface is largely unknown. In this study, we aimed to develop a process-based isotope model underpinning the competition of abiotic (sorption, desorption, and ion exchange) and biotic (uptake, metabolism, and release) reactions during uptake and recycling of ferrihydrite-bound P by E. coli. Our model comprises equations describing the partitioning relationship among different P pools and their corresponding oxygen isotope compositions and is based exclusively on oxygen isotope exchange at multiple sites including mineral surface, aqueous phase, and bacterial cells. The process-based model adequately reproduced the measured concentration and isotope compositions over time. Furthermore, parametric and sensitivity analyses using the model indicated that the rate of biological uptake of P was the major factor controlling the changes of phosphate isotope composition. In conclusion, our model provides new insights into a mechanistic aspect of isotope exchange and could be potentially useful for future efforts to understand the interplay of biotic and abiotic factors on phosphorus cycling in natural environments.

Evaluation of the information content of long-term wastewater characteristics data in relation to activated sludge model parameters.

A parameter estimation framework was used to evaluate the ability of observed data from a full-scale nitrification-denitrification bioreactor to reduce the uncertainty associated with the bio-kinetic and stoichiometric parameters of an activated sludge model (ASM). Samples collected over a period of 150 days from the effluent as well as from the reactor tanks were used. A hybrid genetic algorithm and Bayesian inference were used to perform deterministic and parameter estimations, respectively. The main goal was to assess the ability of the data to obtain reliable parameter estimates for a modified version of the ASM. The modified ASM model includes methylotrophic processes which play the main role in methanol-fed denitrification. Sensitivity analysis was also used to explain the ability of the data to provide information about each of the parameters. The results showed that the uncertainty in the estimates of the most sensitive parameters (including growth rate, decay rate, and yield coefficients) decreased with respect to the prior information.

Dual substrate limitation modeling and implications for mainstream deammonification.

Substrate limitation occurs frequently in wastewater treatment and knowledge about microbial behavior at limiting conditions is essential for the use of biokinetic models in system design and optimization. Monod kinetics are well-accepted for modeling growth rates when a single substrate is limiting, but several models exist for treating two or more limiting substrates simultaneously. In this study three dual limitation models (multiplicative, minimum, and Bertolazzi) were compared based on experiments using nitrite-oxidizing bacteria (limited by dissolved oxygen and nitrite) and ANaerobic AMMonia-OXidizing bacteria or Aanammox (limited by ammonium and nitrite) within mixed liquor from deammonification pilots. A deterministic likelihood-based parameter estimation followed by Bayesian inference was used to estimate model-specific parameters. The minimum model outperformed the other two by a slight margin in three separate analyses. 1) Parameters estimated using the minimum model were closest to parameters estimated from single limitation batch tests. 2) Among simulations based on each model's own estimated parameters, the minimum model best described the experimental observations. 3) Among simulations based on parameters estimated from single limitation, the minimum model best described the experimental observations. The dual substrate model selected among the three studied can effect a 75% process performance variation based on simulations of a full-scale mainstream deammonification system.

Assessment of the endogenous respiration rate and the observed biomass yield for methanol-fed denitrifying bacteria under anoxic and aerobic conditions.

In this study, the endogenous respiration rate and the observed biomass yield of denitrifying methylotrophic biomass were estimated through measuring changes in denitrification rates (DNR) as a result of maintaining the biomass under methanol deprived conditions. For this purpose, activated sludge biomass from a full-scale wastewater treatment plant was kept in 10-L batch reactors for 8 days under fully aerobic and anoxic conditions at 20 °C without methanol addition. To investigate temperature effects, another biomass sample was placed under starvation conditions over a period of 10 days under aerobic conditions at 25 °C. A series of secondary batch tests were conducted to measure DNR and observed biomass yields. The decline in DNR over the starvation period was used as a surrogate to biomass decay rate in order to infer the endogenous respiration rates of the methylotrophs. The regression analysis on the declining DNR data shows 95% confidence intervals of 0.130 ± 0.017 day-1 for endogenous respiration rate under aerobic conditions at 20 °C, 0.102 ± 0.013 day-1 under anoxic conditions at 20 °C, and 0.214 ± 0.044 day-1 under aerobic conditions at 25 °C. Results indicated that the endogenous respiration rate of methylotrophs is 20% slower under anoxic conditions than under aerobic conditions, and there is a significant temperature dependency, with an Arrhenius coefficient of 1.10. The observed biomass yield value showed an increasing trend from approximately 0.2 to 0.6 when the starvation time increased from 0 to 10 days.

A flexible modeling framework for hydraulic and water quality performance assessment of stormwater green infrastructure.

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructure (GI) practices. The framework conceptualizes GI practices using blocks (spatial features) and connectors (interfaces) representing functional components of a GI. The blocks represent spatial features with the ability to store water (e.g., pond, soil, benthic sediments, manhole, or a generic storage zone) and water quality constituents including chemical constituents and particles. The hydraulic module can solve a combination of Richards equation, kinematic/diffusive wave, Darcy, and other user-provided flow models. The particle transport module is based on performing mass-balance on particles in different phases, e.g., mobile and deposited in soil with constitutive theories controlling their transport, settling, deposition, and release. The reactive transport modules allow constituents to be in dissolved, sorbed, bound to particles, and undergo user-defined transformations. Four applications of the modeling framework are presented that demonstrate its flexibility for simulating urban GI performance.

Storm Water Management Model: Performance Review and Gap Analysis.

The storm water management model (SWMM) is a widely used tool for urban drainage design and planning. Hundreds of peer-reviewed articles and conference proceedings have been written describing applications of SWMM. This review focuses on collecting information on model performance with respect to calibration and validation in the peer-reviewed literature. The major developmental history and applications of the model are also presented. The results provide utility to others looking for a quick reference to gauge the integrity of their own unique SWMM application. A gap analysis assesses the model's ability to perform water-quality simulations considering green infrastructure (GI)/low impact development (LID) designs and effectiveness. It is concluded that the level of detail underlying the conceptual model of SWMM versus its overall computational parsimony is well balanced-making it an adequate model for large and medium-scale hydrologic applications. However, embedding a new mechanistic algorithm or providing user guidance for coupling with other models will be necessary to realistically simulate diffuse pollutant sources, their fate and transport, and the effectiveness of GI/LID implementation scenarios.

Swimming Motility Reduces Deposition to Silica Surfaces.

The transport and fate of bacteria in porous media is influenced by physicochemical and biological properties. This study investigated the effect of swimming motility on the attachment of cells to silica surfaces through comprehensive analysis of cell deposition in model porous media. Distinct motilities were quantified for different strains using global and cluster-based statistical analyses of microscopic images taken under no-flow condition. The wild-type, flagellated strain DJ showed strong swimming as a result of the actively swimming subpopulation whose average speed was 25.6 µm/s; the impaired swimming of strain DJ77 was attributed to the lower average speed of 17.4 µm/s in its actively swimming subpopulation; and both the nonflagellated JZ52 and chemically treated DJ cells were nonmotile. The approach and deposition of these bacterial cells were analyzed in porous media setups, including single-collector radial stagnation point flow cells (RSPF) and two-dimensional multiple-collector micromodels under well-defined hydrodynamic conditions. In RSPF experiments, both swimming and nonmotile cells moved with the flow when at a distance ≥20 µm above the collector surface. Closer to the surface, DJ cells showed both horizontal and vertical movement, limiting their contact with the surface, while chemically treated DJ cells moved with the flow to reach the surface. These results explain how wild-type swimming reduces attachment. In agreement, the deposition in micromodels was also lowest for DJ compared with those for DJ77 and JZ52. Wild-type swimming specifically reduced deposition on the upstream surfaces of the micromodel collectors. Conducted under environmentally relevant hydrodynamic conditions, the results suggest that swimming motility is an important characteristic for bacterial deposition and transport in the environment.

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo.

One of the most important challenges in making activated sludge models (ASMs) applicable to design problems is identifying the values of its many stoichiometric and kinetic parameters. When wastewater characteristics data from full-scale biological treatment systems are used for parameter estimation, several sources of uncertainty, including uncertainty in measured data, external forcing (e.g. influent characteristics), and model structural errors influence the value of the estimated parameters. This paper presents a Bayesian hierarchical modeling framework for the probabilistic estimation of activated sludge process parameters. The method provides the joint probability density functions (JPDFs) of stoichiometric and kinetic parameters by updating prior information regarding the parameters obtained from expert knowledge and literature. The method also provides the posterior correlations between the parameters, as well as a measure of sensitivity of the different constituents with respect to the parameters. This information can be used to design experiments to provide higher information content regarding certain parameters. The method is illustrated using the ASM1 model to describe synthetically generated data from a hypothetical biological treatment system. The results indicate that data from full-scale systems can narrow down the ranges of some parameters substantially whereas the amount of information they provide regarding other parameters is small, due to either large correlations between some of the parameters or a lack of sensitivity with respect to the parameters.

Bayesian process-identification in bacteria transport in porous media.

A Bayesian parameter estimation approach is developed for the estimation of joint probability distribution functions for colloid and bacterial fate and transport model parameters describing breakthrough curves (BTCs) obtained through porous media column studies, and is applied to data involving different ionic strength solutions to fit models of differing complexity. Our approach focuses on the simultaneous fitting of a number of BTCs representing different conditions, and it provides a measure of the goodness of model structure, namely Deviance Information Criteria (DIC). Comparison of DIC per model fit enables the evaluation of the significance of various processes through step-wise increases in complexity due to the addition of process model components. We use the method to investigate the transport of both flagellated and non-flagellated strains of Azotobacter vinelandii in a simulated porous media under three ionic strengths. Three different model structures are considered: one without a detachment process and with Langmuirian blocking function, one with detachment, and one with detachment and a second-order blocking function based on random sequential adsorption. First, the model was applied separately to each single BTC. Next, the model was applied comprehensively to the experiments under various ionic strengths, whereas some transport parameters including dispersivity, detachment coefficient, the fraction of cells undergoing irreversible attachment, and the coefficient of the second-order blocking term were assumed to be the same under different ionic strengths. In most cases, including detachment substantially improved the DIC as expected, whereas using the second-order blocking improved DIC for most of the cases when the method was applied to separate BTCs but not when the method was applied collectively to the three BTCs obtained under various ionic strengths. Also, comparing the outcomes of the separate applications of the parameter estimation algorithm versus the collective application indicates that the uncertainty associated with the estimated parameters is substantially smaller when the collective approach is used and also that the estimated parameters are more consistent with the expectations based on the underlying physical processes.

Flagella-mediated differences in deposition dynamics for Azotobacter vinelandii in porous media.

A multiscale approach was designed to study the effects of flagella on deposition dynamics of Azotobacter vinelandii in porous media, independent of motility. In a radial stagnation point flow cell (RSPF), the deposition rate of a flagellated strain with limited motility, DJ77, was higher than that of a nonflagellated (Fla(-)) strain on quartz. In contrast, Fla(-) strain deposition exceeded that of DJ77 in two-dimensional silicon microfluidic models (micromodels) and in columns packed with glass beads. Both micromodel and column experiments showed decreasing deposition over time, suggesting that approaching cells were blocked from deposition by previously deposited cells. Modeling results showed that blocking became effective for DJ77 strain at lower ionic strengths (1 mM and 10 mM), while for the Fla(-) strain, blocking was similar at all ionic strengths. In late stages of micromodel experiments, ripening effects were also observed, and these appeared earlier for the Fla(-) strain. In RSPF and column experiments, deposition of the flagellated strain was influenced by ionic strength, while ionic strength dependence was not observed for the Fla(-) strain. The observations in all three setups suggested flagella affect deposition dynamics and, in particular, result in greater sensitivity to ionic strength.

A stochastic stormwater quality volume-sizing method with first flush emphasis.

A Monte Carlo simulation technique was applied to assess the effect of stormwater quality volume captured by best management practices (BMPs) on the frequency of discharging concentrations of constituents above certain designated threshold limits. The method used an assumption of a power law relationship between the cumulative load and flow to incorporate the first flush effect. The exponent of this relationship was considered a random variable and its frequency distribution was obtained from 78 measured pollutographs from three urban highway sites in West Los Angeles, California. Although the effect of rain depth captured by BMPs is site-specific, the method offered here provides a systematic approach to evaluate the effect of selecting various regulatory guidelines for controlling urban stormwater pollution on the overall discharge of pollutants into waterways. This allows selecting the requirements for capturing runoff volume by BMPs based on the tradeoff between the probability of concentration criteria violation and economic factors.